Chlorophyll Concentration#

../../../_images/738aef64788b0581d2161676a5f6666666b167b47c1b55920a4ae35709c6dc0f.png

Figure. Change in Chlorophyll from satellite. The map (top) shows the change in annual mean Chlorophyll-a concentration (mg/m3) in the vicinity of Palau over the period 1998-2024 derived from satellite remotely sensed ocean color data. The grey line is the Palau EEZ. The line plot (bottom) shows the change in mean Chlorophyll-a concentration (mg/m3) averaged over the area within the top plot. The black line represents the trend, which is not statistically significant (p < 0.05).

Hide code cell source
import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys

import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from myst_nb import glue 

sys.path.append("../../../../indicators_setup")

from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
from ind_setup.plotting import plot_base_map, plot_map_subplots, add_oni_cat, plot_bar_probs, fontsize

sys.path.append("../../../functions")
from data_downloaders import download_ERDDAP_data, download_oni_index

Setup#

Define area of interest

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

EEZ shapefile

shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

Download Data#

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
Hide code cell source
base_url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-monthly-v6-0.csv'
dataset_id = 'chlor_a'
if update_data:
    date_ini = '1998-01-01T00:00:00Z'
    date_end = '2024-12-01T00:00:00Z'
    data = download_ERDDAP_data(base_url, dataset_id, date_ini, date_end, lon_range, lat_range)
    data_xr = data.set_index(['latitude', 'longitude', 'time']).to_xarray()
    data_xr['time'] = pd.to_datetime(data_xr.time)
    data_xr = data_xr.coarsen(longitude=2, latitude=2, boundary = 'pad').mean()
    data_xr.to_netcdf(op.join(path_data, f'griddap_{dataset_id}.nc'))
else:
    data_xr = xr.open_dataset(op.join(path_data, f'griddap_{dataset_id}.nc'))

Analysis#

Average#

fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'Greens', vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 1), 
                vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 99))
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label='chlorophyll (mg/$m^3$)')
glue("average_map", fig, display=False)
plt.savefig(op.join(path_figs, 'F15_chlorophyll_mean_map.png'), dpi=300, bbox_inches='tight')
../../../_images/738aef64788b0581d2161676a5f6666666b167b47c1b55920a4ae35709c6dc0f.png

Annual average#

data_y = data_xr.resample(time='1YE').mean()
fig = plot_map_subplots(data_y, dataset_id, shp_eez = shp_eez, cmap = 'Greens', vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 1), 
                vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 99), cbar = 1)
../../../_images/b9e5839848cee7171b9d7b2d47eff1b86ac0685db6a5248f16ac7b8d8fb1d603.png

Annual anomaly#

data_an = data_y - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.1, vmax=.1, cbar = 1)
../../../_images/0fce5ba655a5ee3e1647bc47049444b6de4e5c3978b031221a608b40f89efe59.png

Average over area#

dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : 'Chlorophyll - MEAN AREA'},]
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), label_yaxes = 'Chlorophyll (mg/m3)');
fig.write_html(op.join(path_figs, 'F15_chlorophyll_mean_trend.html'), include_plotlyjs="cdn")
d_p = data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe()

Timeseries at a given point#

loc = [7.37, 134.7]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'Chlorophyll at [{loc[0]}, {loc[1]}]'},]
fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()
<matplotlib.legend.Legend at 0x18bd24ec0>
../../../_images/038a2757803511e8b284fa11a57d0ba084269dbabe56e09215299e69572e231d.png
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), label_yaxes = 'Chlorophyll (mg/m3)');

ONI index analysis#

p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)
df1 = add_oni_cat(df1, lims = lims)
df1['ONI'] = df1['oni_cat']
data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()

Average#

fig = plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'Greens', 
                  vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 1), 
                  vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 99),
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)
plt.savefig(op.join(path_figs, 'F15_chlorophyll_ENSO.png'), dpi=300, bbox_inches='tight')
../../../_images/a40ff19e94167c234f78c1ecaa0cd5bc60008a31fd541d739b99fddb4e7f2dac.png

Anomaly#

data_an = data_oni - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.05, vmax=.05,
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)
../../../_images/8e8692e5135a30f29f29168d12c4da16c80ac2edf04efca574f49eb9c9542b77.png